Triple
T3250647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Game of Thrones season 1 |
E68167
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object | Emilia Clarke |
E49606
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Emilia Clarke | Statement: [Game of Thrones season 1, mainCastMember, Emilia Clarke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emilia Clarke Context triple: [Game of Thrones season 1, mainCastMember, Emilia Clarke]
-
A.
Emilia Clarke
chosen
Emilia Clarke is an English actress best known for her role as Daenerys Targaryen in the television series "Game of Thrones."
-
B.
Lena Headey
Lena Headey is an English actress best known for playing Cersei Lannister in the television series "Game of Thrones."
-
C.
Maisie Williams
Maisie Williams is an English actress best known for her breakout role as Arya Stark in the television series "Game of Thrones."
-
D.
Sophie Turner
Sophie Turner is an English actress best known for her role as Sansa Stark in the television series "Game of Thrones."
-
E.
Gwendoline Christie
Gwendoline Christie is an English actress best known for her role as Brienne of Tarth in the television series "Game of Thrones" and as Captain Phasma in the "Star Wars" sequel trilogy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf40f7908190a450c3136fccb020 |
completed | March 8, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e82f91788190a9b14613eab7a439 |
completed | March 12, 2026, 4:22 p.m. |
Created at: March 8, 2026, 3:09 p.m.